This chapter is from the book

We live in a world with a surfeit of information at our service. It is our choice whether we seek out data that reinforce our biases or choose to look at the world in a critical, rational manner, and allow reality to bend our preconceptions. In the long run, the truth will work better for us than our cherished fictions.

—Razib Khan, “The Abortion Stereotype,” The New York Times (January 2, 2015)

To become a visualization designer, it is advisable to get acquainted with the language of research. Getting to know how the methods of science work will help us ascertain that we’re not being fooled by our sources. We will still be fooled on a regular basis, but at least we’ll be better equipped to avoid it if we’re careful.

Up to this point I’ve done my best to prove that interpreting data and visualizations is to a great extent based on applying simple rules of thumb such as “compared to what/who/where/when,” “always look for the pieces that are missing in the model,” and “increase depth and breadth up to a reasonable point.” I stressed those strategies first because in the past two decades I’ve seen that many designers and journalists are terrified by science and math for no good reason.1

It’s time to get a bit more technical.

The Scientific Stance

Science isn’t only what scientists do. Science is a stance, a way to look at the world, that everybody and anybody, regardless of cultural origins or background, can embrace—I’ll refrain from writing “should,” although I feel tempted. Here’s one of my favorite definitions: “Science is a systematic enterprise that builds, organizes, and shares knowledge in the form of testable explanations and predictions.”2Science is, then, a set of methods, a body of knowledge, and the means to communicate it.

Scientific discovery consists of an algorithm that, in a highly idealized form, looks like this:

You grow curious about a phenomenon, you explore it for a while, and then you formulate a plausible conjecture to describe it, explain it, or predict its behavior. This conjecture is just an informed hunch for now.

You transform your conjecture into a formal and testable proposition, called a hypothesis.

You thoroughly study and measure the phenomenon (under controlled conditions whenever it’s possible). These measurements become data that you can use to test your hypothesis.

You draw conclusions, based on the evidence you have obtained. Your data and tests may force you to reject your hypothesis, in which case you’ll need go to back to the beginning. Or your hypothesis may be tentatively corroborated.

Eventually, after repeated tests and after your work has been reviewed by your peers, members of your knowledge or scientific community, you may be able to put together a systematic set of interrelated hypotheses to describe, explain, or predict phenomena. We call this a theory. From this point on, always remember what the word “theory” really means. A theory isn’t just a careless hunch.

These steps may open researchers’ eyes to new paths to explore, so they don’t constitute a process with a beginning and an end point but a loop. As you’re probably guessing, we are returning to themes we’ve already visited in this book: good answers lead to more good questions. The scientific stance will never take us all the way to an absolute, immutable truth. What it may do—and it does it well—is to move us further to the right in the truth continuum.